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The magnetohydrodynamics tangent hyperbolic nanofluid (THNF) flow with the mutual impact of melting heat transfer and wedge angle over a permeable wedge is investigated numerically in the present study. Electronic devices generate excessive heat during operations, so THNF is often employed to regulate them. THNF has the ability to neutralize heat with greater efficacy, thereby reducing the probability of overheating. The influence of thermal radiation, Soret and Dufour, and heat source/sink is also observed on the fluid flow. The modeled equations are simplified to the lowest order through the similarity conversion. The obtained set of dimensionless equations is further calculated numerically by employing the parametric continuation method. The computational findings of the present study are compared to the published results for accuracy purposes. It has been detected that the results are precise and reputable. Moreover, from the graphical results, it has been perceived that the effect of permeability factor (K p) reduces the fluid flow. The rising effect of wedge angle factor enhances the energy dissemination rate and shearing stress; however the augmentation of Weissenberg number drops skin friction and energy transference rate.

Present study was conducted to investigate the content of toxic heavy metals; lead, cadmium, manganese, nickel and zinc in organs of edible fish of River Ravi. Samples of Labeo rohita, Cyprinus carpio, Gibelion catla and Cirrhinus mrigala were captured from two sites at River Ravi i.e. Kala Khatai and Head Balloki. Zinc and lead content was higher in organs of fish specimens collected from Head Balloki than those collected from Village Kala Khatai. Higher average cadmium content was detected in most of the organs of fish collected from Head Balloki than those of Village Kala Khatai. However, higher average manganese content was found in examined organs of fish captured from Village Kala Khatai than those of Head Balloki. Lead and cadmium content in muscles of fish collected from Head Balloki was higher than acceptable level proposed by European Commission (0.2 µg/g and 0.05 µg/g, respectively). Hepatosomatic index of fish species (except L. rohita) collected from Head Balloki was lower than those of Village Kala Khatai. Study has led to the conclusion that edible fish of River Ravi is contaminated with toxic metals and contamination level is higher in fish at Head Balloki as compared to that observed at Village Kala Khatai. [ABSTRACT FROM AUTHOR]

Present study was based on determination of soil quality to assess its suitability for aquaculture activities from thirty-one selected areas situated in four division of Punjab. Results indicated that soil particle size distribution was suitable for construction of fish ponds at most of the sites in Lahore and Sahiwal. In Gujranwala, about 50% of soil samples contained higher sand content (> 60%). Clay particles were found to be less than 25% in 75% of the soil samples whereas sand content was higher than 50% in 25% of the samples collected from Sargodha division. Higher soil pH (> 7.0) found at all sites showed calcareous nature of soil and indicated that liming soil to increase pH was not a necessity in these areas. Soil at all the studied areas of Sargodha was found to be saline or very strongly saline except at Village Hernali in Mianwali district. It was found that about 83%, 100% and 80% of sites studied in Lahore, Sahiwal and Sargodha can be used for fish culture respectively. However, none of the site in Gujranwala was found to be suitable for aquaculture if used in present form. Appropriate soil management techniques can be adopted to use the land with unsuitable soil particle size distribution for aquaculture. Saline nature of soil in Sargodha division must be considered while selecting cultureable species of fish/shrimps in these areas. [ABSTRACT FROM AUTHOR]

The study was designed to assess the effects of periphyton grown on different substrates on water quality and production of major carps cultured in zero water exchange system. Three experimental treatments included; T1 (Control): without substrate, T2: using PVC pipes substrates, T3, using Bamboo poles substrates. Fish seed of Labeo rohita: Cirrhinus mrigala and Gibelion catla were stocked in cemented tanks in 1:1:1 ratio at stocking density of 3 fishm-2 for 60 days period. Survival rate of L. rohita, C. mrigala and G. catla was significantly higher (p<0.05) in substrates based treatments. In T1, T2 & T3, it was found to be 34.09%, 81.82% & 90.91% for L. rohita; 22.73%, 100.0% & 100.0% for C. mrigala and 36.36%, 90.91% & 100.0% for G. catla respectively. Total fish biomass of L. rohita, C. mrigala and G. catla harvested at the end of trial was 2.24, 4.32 and 1.42 times higher in treatment with bamboo poles as substrates and 1.18, 3.64 and 0.75 time higher in PVC pipes based treatment than that of control. Concentration of ammonia and nitrite was found to be significantly low in treatments with periphyton than control. High fish survival in periphyton based systems can be attributed to bioremediation through bioprocesses of their ubiquitous microbial biofilms. [ABSTRACT FROM AUTHOR]

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Present study aimed to investigate phytoconstituents, total phenolic and flavonoids content, antioxidant activity and biochemical composition of the leaf extract of Artemisia argyi L. Qualitative analysis was conducted using standard methods however, total phenolic, flavonoids content and antioxidant activity was assessed by Folin-Ciocalteu, aluminium chloride colourimetric method and 1, 1- Diphenyl-2-picrylhydrazyl (DPPH) assay, respectively. Compositional analysis was carried out by Gas chromatography-mass spectrometry (GC-MS). The outcome of the qualitative analysis suggested the presence of flavonoids, phenols, terpenoids, steroids, saponins, tannins and flavones except for alkaloids and glycosides. However, total phenols recorded were 16.89, 7.45 and 3.63 mg gallic acid equivalent GAE/g; flavonoids 20.80, 7.13 and 2.42 mg quercetin equivalent QE/g and DPPH inhibition percent was 81.48, 65.62 and 57.78% from 1st, 2nd and 3rd extraction, respectively. GC-MS analysis exposed the existence of ten biological compounds corresponding to 99.91% of the total extract. However, erucylamide (33.42%), 1-decene, 4-methyl- (12.63%), myo-Inositol, (10.42%), α-Cadinol (9.13%) and 2- pyrrolidinone (8.68%) were the major compounds with five minor compounds. It was concluded that the leaves of A. argyi contain biological constituents responsible for antioxidant properties which can be introduced as a natural antioxidant pharmacologically and as botanical alternative of synthetic chemicals. However further studies are required on identification of specific components responsible for such activities. [ABSTRACT FROM AUTHOR]

In the present study, 85 strains of actinomycetes were isolated from the neem tree (Azadirachta indica) and screened for their insecticidal activity against the green peach aphid, Myzus persicae. The results showed that crude extracts from 24 strains exerted a contact effect against M. persicae with different insecticidal efficacies. Crude extracts from 8 actinomycetes strains exhibited the highest insecticidal activity (above 60%). Out of these 8 strains, 3 isolates that produced the maximum mortalities were screened a second time. The crude extract from strain G30 was the most virulent against the green peach aphid, with LC50 and LC95 values of 1.680 mg/mL and 4.370 mg/mL, respectively, after 48 h of treatment. The following morphological, culture, physiological and biochemical characteristics of strain G30 were recorded: (i) ovateorbicular and smooth surface spores with short and curve filaments; (ii) an aerial off-white mycelium with a mustered yellow base; (iii) inability to produce soluble pigments; (iv) the ability to hydrolyze starch but not cellulose; (v) the ability to utilize glycerin and several sugars as a carbon source but not L-rhamnose and sorbitol. Molecular identification of G30 revealed a 99.6% genetic similarity of the 16S rDNA sequence with Streptomyces albidoflavus. We conclude that the isolate G30 was S. albidoflavus and that the insecticidal activity of its crude extract was sufficiently high to become a candidate for bioinsecticide development.

The optimization of fermentation conditions is necessary for field application of biological control agents. The present study was designed to optimize the fermentation conditions for the Bacillus aerius strain, JS-786 in terms of cell dry mass and its antifungal activity against Botrytis cinerea with response surface methodology. A strain of bacteria with strong antifungal activity was isolated from the phyllosphere of tomato plant and identified as B. aerius JS-786 based on the sequence homology of its 16S rRNA gene. After the success of preliminary antifungal activity tests, response surface methodology was used to optimize the fermentation conditions (medium pH, gelatin percentage, incubation period, rotatory speed and incubation temperature) to maximize the cell dry mass and antifungal activity against B. cinerea. A 25 factorial central composite design was employed and multiple response optimization was used to determine the desirability of the operation. The results of regression analysis showed that at the individual level, all of the experimental parameters were significant for cell dry mass; significant results were obtained for antifungal activity pH, incubation period, rotatory speed and incubation temperature. The interactive effect of the incubation period, rotatory speed and incubation temperature was significant. Maximum cell dry mass (8.7 g/L) and inhibition zone (30.4 mm) were obtained at pH 6.4, gelatin 3.2%, incubation period 36.92 h, rotatory speed 163 rpm, and temperature 33.5°C. This study should help to formulate a more rational and cost-effective biological product both in terms of bacterial growth and antifungal activity.

Acetolactate synthase (ALS)-inhibiting herbicides play an important role in controlling broad-leaved weeds. Populations of Sagittaria trifolia L. showed resistance to ALS-inhibiting sulfonylurea herbicides (e.g. bensulfuron-methyl) in paddy fields in the northeast of China. In our study, whole-plant bioassays were performed on eight suspected resistant S. trifolia populations that showed high levels of resistance to bensulfuron-methyl, with resistance indices from 31.06 to 120.35. The results of ALS-activity assays were consistent with the observed whole-plant dose-response data. This confirmed that resistant populations displayed significantly higher ALS activity than the sensitive population due to prevention of normal enzyme-herbicide interaction. The mutations Pro-197-Ser, Pro-197-His, Pro-197-Thr and Pro-197-Leu were identified in the ALS gene of resistant populations. Pro-197-His and Pro-197-Thr mutations conferring resistance to bensulfuron-methyl are reported for the first time in S. trifolia. All resistant populations were resistant to sulfonylurea (SU) herbicides, but not to imidazolinone (IMI) herbicides. HLJ-5 and JL-3 populations were resistant to bispyribac-sodium of the pyrimidinyl-thiobenozoate (PTB) class of ALS herbicides, JL-2 to penoxsulam of triazolopyrimidine (TP) class and JL-1 to pyribenzoxim, also of PTB class. The eight S. trifolia populations were susceptible to other herbicide modes of action tested.

Introduction: Pharmacovigilance (PV), or the ongoing safety monitoring after a medication has been licensed, plays a crucial role in pregnancy, as clinical trials often exclude pregnant people. It is important to understand how pregnancy PV projects operate in low- and middle-income countries (LMICs), where there is a disproportionate lack of PV data yet a high burden of adverse pregnancy outcomes. We conducted a scoping review to assess how exposures and outcomes were measured in recently published pregnancy PV projects in LMICs., Methods: We utilized a search string, secondary review, and team knowledge to review publications focusing on therapeutic or vaccine exposures among pregnant people in LMICs. We screened abstracts for relevance before conducting a full text review, and documented measurements of exposures and outcomes (categorized as maternal, birth, or neonatal/infant) among other factors, including study topic, setting, and design, comparator groups, and funding sources., Results: We identified 31 PV publications spanning at least 24 LMICs, all focusing on therapeutics or vaccines for infectious diseases, including HIV (n = 17), tuberculosis (TB; n = 9), malaria (n = 7), pertussis, tetanus, and diphtheria (n = 1), and influenza (n = 3). As for outcomes, n = 15, n = 31, and n = 20 of the publications covered maternal, birth, and neonatal/infant outcomes, respectively. Among HIV-specific publications, the primary exposure-outcome relationship of focus was exposure to maternal antiretroviral therapy and adverse outcomes. For TB-specific publications, the main exposures of interest were second-line drug-resistant TB and isoniazid-based prevention therapeutics for pregnant people living with HIV. For malaria-specific publications, the primary exposure-outcome relationship of interest was antimalarial medication exposure during pregnancy and adverse outcomes. Among vaccine-focused publications, the exposure was assessed during a specific time during pregnancy, with an overall interest in vaccine safety and/or efficacy. The study settings were frequently from Africa, designs varied from cohort or cross-sectional studies to clinical trials, and funding sources were largely from high-income countries., Conclusion: The published pregnancy PV projects were largely centered in Africa and concerned with infectious diseases. This may reflect the disease burden in LMICs but also funding priorities from high-income countries. As the prevalence of non-communicable diseases increases in LMICs, PV projects will have to broaden their scope. Birth and neonatal/infant outcomes were most reported, with fewer reporting on maternal outcomes and none on longer-term child outcomes; additionally, heterogeneity existed in definitions and ascertainment of specific measures. Notably, almost all projects covered a single therapeutic exposure, missing an opportunity to leverage their projects to cover additional exposures, add scientific rigor, create uniformity across health services, and bolster existing health systems. For many publications, the timing of exposure, specifically by trimester, was crucial to maternal and neonatal safety. While currently published pregnancy PV literature offer insights into the PV landscape in LMICs, further work is needed to standardize definitions and measurements, integrate PV projects across health services, and establish longer-term monitoring., (© 2024. The Author(s).)

Introduction: The rapid advancement of science and technology has significantly expanded the capabilities of artificial intelligence, enhancing diagnostic accuracy for gastric cancer., Methods: This research aims to utilize endoscopic images to identify various gastric disorders using an advanced Convolutional Neural Network (CNN) model. The Kvasir dataset, comprising images of normal Z-line, normal pylorus, ulcerative colitis, stool, and polyps, was used. Images were pre-processed and graphically analyzed to understand pixel intensity patterns, followed by feature extraction using adaptive thresholding and contour analysis for morphological values. Five deep transfer learning models-NASNetMobile, EfficientNetB5, EfficientNetB6, InceptionV3, DenseNet169-and a hybrid model combining EfficientNetB6 and DenseNet169 were evaluated using various performance metrics., Results & Discussion: For the complete images of gastric cancer, EfficientNetB6 computed the top performance with 99.88% accuracy on a loss of 0.049. Additionally, InceptionV3 achieved the highest testing accuracy of 97.94% for detecting normal pylorus, while EfficientNetB6 excelled in detecting ulcerative colitis and normal Z-line with accuracies of 98.8% and 97.85%, respectively. EfficientNetB5 performed best for polyps and stool with accuracies of 98.40% and 96.86%, respectively.The study demonstrates that deep transfer learning techniques can effectively predict and classify different types of gastric cancer at early stages, aiding experts in diagnosis and detection., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Bhardwaj, Kim, Koul, Kumar, Changela, Shafi and Ijaz.)

Background: Sexual violence (SV) increases HIV susceptibility in a sustained manner. This study evaluated genital cytokines and colposcopy findings in women reporting both recent and more remote SV.Methods: A cross-sectional study of HIV-1 negative Kenyan women who engage in sex work (WESW) was performed. Cervicovaginal fluid was collected by menstrual cup and cytokines (IFNγ, TNFα, IL-1β, IL-6, IL-10, MIP-1α, MIP-1β and CXCL10) measured using chemiluminescence. Cervical injury was assessed by colposcopy. Associations between recent (≤30 days prior), more remote (>30 days prior) and no (reference category) SV exposure and cytokine concentrations were evaluated using linear regression., Results: Among 282 participants, 25 (8.9%) reported recent SV and 123 (43.6%) reported more remote SV. Only two cytokines (IL-10 and CXCL10) were associated with the 3-category SV variable in bivariable modeling at the pre-specified cut-off (p < 0.2) and carried forward. In multivariable analyses, more remote SV (β = 0.72, 95% CI 0.06, 1.38; p = 0.03), but not recent SV (β = 0.20, 95%CI -0.99, 1.39; p = 0.74) was associated with cervicovaginal IL-10 compared to no SV. Recent (β = 0.36, 95% CI -0.94, 1.67; p = 0.58) and more remote (β = 0.51, 95% CI -0.21, 1.24; p = 0.16) SV were not associated with CXCL10 compared to no SV. Cervical epithelial friability (χ2 = 1.3, p = 0.51), erythema (χ2 = 2.9, p = 0.24), vascular disruption (χ2 = 1.4; p = 0.50), epithelial disruption (χ2 = 2.6, p = 0.27), or any colposcopy finding (χ2 = 1.2, p = 0.54) were not associated with SV category by chi-square test., Conclusions: The mechanism linking SV to sustained increases in HIV susceptibility may not be related to persistent genital inflammation or injury., Competing Interests: Conflicts of interest: RSM receives research funding, paid to the University of Washington, from Hologic Corporation. JO is on the scientific advisory board of Aerium Therapeutics. For the remaining authors, no conflicts of interest were declared., (Copyright © 2024 American Sexually Transmitted Diseases Association. All rights reserved.)

Identifying and recognizing the food on the basis of its eating sounds is a challenging task, as it plays an important role in avoiding allergic foods, providing dietary preferences to people who are restricted to a particular diet, showcasing its cultural significance, etc. In this research paper, the aim is to design a novel methodology that helps to identify food items by analyzing their eating sounds using various deep learning models. To achieve this objective, a system has been proposed that extracts meaningful features from food-eating sounds with the help of signal processing techniques and deep learning models for classifying them into their respective food classes. Initially, 1200 audio files for 20 food items labeled have been collected and visualized to find relationships between the sound files of different food items. Later, to extract meaningful features, various techniques such as spectrograms, spectral rolloff, spectral bandwidth, and mel-frequency cepstral coefficients are used for the cleaning of audio files as well as to capture the unique characteristics of different food items. In the next phase, various deep learning models like GRU, LSTM, InceptionResNetV2, and the customized CNN model have been trained to learn spectral and temporal patterns in audio signals. Besides this, the models have also been hybridized i.e. Bidirectional LSTM + GRU and RNN + Bidirectional LSTM, and RNN + Bidirectional GRU to analyze their performance for the same labeled data in order to associate particular patterns of sound with their corresponding class of food item. During evaluation, the highest accuracy, precision,F1 score, and recall have been obtained by GRU with 99.28%, Bidirectional LSTM + GRU with 97.7% as well as 97.3%, and RNN + Bidirectional LSTM with 97.45%, respectively. The results of this study demonstrate that deep learning models have the potential to precisely identify foods on the basis of their sound by computing the best outcomes., (© 2024. The Author(s).)

Parasitic organisms pose a major global health threat, mainly in regions that lack advanced medical facilities. Early and accurate detection of parasitic organisms is vital to saving lives. Deep learning models have uplifted the medical sector by providing promising results in diagnosing, detecting, and classifying diseases. This paper explores the role of deep learning techniques in detecting and classifying various parasitic organisms. The research works on a dataset consisting of 34,298 samples of parasites such as Toxoplasma Gondii, Trypanosome, Plasmodium, Leishmania, Babesia, and Trichomonad along with host cells like red blood cells and white blood cells. These images are initially converted from RGB to grayscale followed by the computation of morphological features such as perimeter, height, area, and width. Later, Otsu thresholding and watershed techniques are applied to differentiate foreground from background and create markers on the images for the identification of regions of interest. Deep transfer learning models such as VGG19, InceptionV3, ResNet50V2, ResNet152V2, EfficientNetB3, EfficientNetB0, MobileNetV2, Xception, DenseNet169, and a hybrid model, InceptionResNetV2, are employed. The parameters of these models are fine-tuned using three optimizers: SGD, RMSprop, and Adam. Experimental results reveal that when RMSprop is applied, VGG19, InceptionV3, and EfficientNetB0 achieve the highest accuracy of 99.1% with a loss of 0.09. Similarly, using the SGD optimizer, InceptionV3 performs exceptionally well, achieving the highest accuracy of 99.91% with a loss of 0.98. Finally, applying the Adam optimizer, InceptionResNetV2 excels, achieving the highest accuracy of 99.96% with a loss of 0.13, outperforming other optimizers. The findings of this research signify that using deep learning models coupled with image processing methods generates a highly accurate and efficient way to detect and classify parasitic organisms., (© 2024. The Author(s).)

The emergence of federated learning (FL) technique in fog-enabled healthcare system has leveraged enhanced privacy towards safeguarding sensitive patient information over heterogeneous computing platforms. In this paper, we introduce the FedHealthFog framework, which was meticulously developed to overcome the difficulties of distributed learning in resource-constrained IoT-enabled healthcare systems, particularly those sensitive to delays and energy efficiency. Conventional federated learning approaches face challenges stemming from substantial compute requirements and significant communication costs. This is primarily due to their reliance on a singular server for the aggregation of global data, which results in inefficient training models. We present a transformational approach to address these problems by elevating strategically placed fog nodes to the position of local aggregators within the federated learning architecture. A sophisticated greedy heuristic technique is used to optimize the choice of a fog node as the global aggregator in each communication cycle between edge devices and the cloud. The FedHealthFog system notably accounts for drop in communication latency of 87.01%, 26.90%, and 71.74%, and energy consumption of 57.98%, 34.36%, and 35.37% respectively, for three benchmark algorithms analyzed in this study. The effectiveness of FedHealthFog is strongly supported by outcomes of our experiments compared to cutting-edge alternatives while simultaneously reducing number of global aggregation cycles. These findings highlight FedHealthFog's potential to transform federated learning in resource-constrained IoT environments for delay-sensitive applications., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

The steady two-dimension (2D) ternary nanofluid (TNF) flow across an inclined permeable cylinder/plate is analyzed in the present study. The TNF flow has been examined under the consequences of heat source/sink, permeable medium and mixed convection. For the preparation of TNF, the magnesium oxide (MgO), cobalt ferrite (CoFe 2 O 4 ) and titanium dioxide (TiO 2 ) are dispersed in water. The rising need for highly efficient cooling mechanisms in several sectors and energy-related processes ultimately inspired the current work. The fluid flow and energy propagation is mathematically described in the form of coupled PDEs. The system of PDEs is reduced into non-dimensional forms of ODEs, which are further numerically handled through the Matlab package (bvp4c). It has been observed that the results display that the porosity factor advances the thermal curve, whereas drops the fluid velocity. The effect of heat source/sink raises the energy field. Furthermore, the plate surface illustrates a leading behavior of energy transport over cylinder geometry versus the variation of ternary nanoparticles (NPs). The energy dissemination rate in the cylinder enhances from 4.73 to 11.421%, whereas for the plate, the energy distribution rate boosts from 6.37 to 13.91% as the porosity factor varies from 0.3 to 0.9., (© 2023. The Author(s).)

In this article, a low-complexity VLSI architecture based on a radix-4 hyperbolic COordinate Rotion DIgital Computer (CORDIC) is proposed to compute the [Formula: see text] root and [Formula: see text] power of a fixed-point number. The most recent techniques use the radix-2 CORDIC algorithm to compute the root and power. The high computation latency of radix-2 CORDIC is the primary concern for the designers. [Formula: see text] root and [Formula: see text] power computations are divided into three phases, and each phase is performed by a different class of the proposed modified radix-4 CORDIC algorithms in the proposed architecture. Although radix-4 CORDIC can converge faster with fewer recurrences, it demands more hardware resources and computational steps due to its intricate angle selection logic and variable scale factor. We have employed the modified radix-4 hyperbolic vectoring (R4HV) CORDIC to compute logarithms, radix-4 linear vectoring (R4LV) to perform division, and the modified scaling-free radix-4 hyperbolic rotation (R4HR) CORDIC to compute exponential. The criteria to select the amount of rotation in R4HV CORDIC is complicated and depends on the coordinates [Formula: see text] and [Formula: see text] of the rotating vector. In the proposed modified R4HV CORDIC, we have derived the simple selection criteria based on the fact that the inputs to R4HV CORDIC are related. The proposed criteria only depend on the coordinate [Formula: see text] that reduces the hardware complexity of the R4HV CORDIC. The R4HR CORDIC shows the complex scale factor, and compensation of such scale factor necessitates the complex hardware. The complexity of R4HR CORDIC is reduced by pre-computing the scale factor for initial iterations and by employing scaling-free rotations for later iterations. Quantitative hardware analysis suggests better hardware utilization than the recent approaches. The proposed architecture is implemented on a Virtex-6 FPGA, and FPGA implementation demonstrates [Formula: see text] less hardware utilization with better error performance than the approach with the radix-2 CORDIC algorithm., (© 2023. The Author(s).)